# Multilevel Weighted Support Vector Machine for Classification on Healthcare Data with Missing Values

arXiv: https://arxiv.org/abs/1604.02123 · HTML: https://mlsanity.com/paper/1604.02123/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Apr 2016.

## Built on by (most cited first, 2 of 2)

- [Lung airway geometry as an early predictor of autism: A preliminary machine learning-based study](https://mlsanity.com/paper/2301.05777/) (2023)
- [Interpretable and Calibrated Classification of Clinical Data Using Supervised Feature Binarization](https://mlsanity.com/paper/2607.15394/) (2026)

## Abstract (arXiv)

This work is motivated by the needs of predictive analytics on healthcare data as represented by Electronic Medical Records. Such data is invariably problematic: noisy, with missing entries, with imbalance in classes of interests, leading to serious bias in predictive modeling. Since standard data mining methods often produce poor performance measures, we argue for development of specialized techniques of data-preprocessing and classification. In this paper, we propose a new method to simultaneously classify large datasets and reduce the effects of missing values. It is based on a multilevel framework of the cost-sensitive SVM and the expected maximization imputation method for missing values, which relies on iterated regression analyses. We compare classification results of multilevel SVM-based algorithms on public benchmark datasets with imbalanced classes and missing values as well as real data in health applications, and show that our multilevel SVM-based method produces fast, and more accurate and robust classification results.